Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/3644—Constructional arrangements
  • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
  • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing

Definitions

• the present invention relates to the implementation of a method and apparatus for estimating battery charge power and discharge power.
• HEVs Hybrid Electric Vehicles
• BEVs Battery Electric Vehicles
• HPPC Hybrid Pulse Power Characterization
• the HPPC method estimates maximum cell power by considering only operational design limits on voltage. It does not consider design limits on current, power, or the battery state-of-charge (SOC) . Also the method produces a crude prediction for horizon At . Each cell in the battery pack is modeled by the approximate relationship
• ⁇ k (t) OCV (z k (t)) - R x i k (t), where OCV(zjt(t)) is the open-circuit-voltage of cell k at its present state-of-charge (zjt(t)) and ⁇ is a constant representing a cell's internal resistance.
• R may be used for charge and discharge currents, if desired, and are denoted as i chg and .R dls , respectively. Since the design limits Vmia - Vk ⁇ - Umax must be enforced, the maximum discharge current may be calculated as constrained by voltage, as shown below
• This prior art charge calculation method is limited in several respects.
• the method does not use operational design limits in SOC, maximum current, or maximum power in the computation.
• the cell model used is too primitive to give precise results. Overly optimistic or pessimistic values could be generated, either posing a safety of battery-health hazard or causing inefficient battery use.
• What is desired is a new method and apparatus for battery charge estimation based on a battery cell model. Such a cell model would be combined with a maximum-power algorithm that uses the cell model to give better power prediction. The new method would also take in operational design limits such as SOC, current, and power.
• Fig. 1A is a flow chart that outlines the maximum discharge estimation according to an embodiment of the present invention
• Fig. IB is a flow chart that outlines the minimum charge estimation according to an embodiment of the present invention
• Fig. 2 is a schematic block diagram showing the sensor components of a power estimating embodiment of the present invention
• Fig. 3 is an example plot of open-circuit-voltage (OCV) as a function of state-of-charge for a particular cell electrochemistry
• Fig. 1A is a flow chart that outlines the maximum discharge estimation according to an embodiment of the present invention
• Fig. IB is a flow chart that outlines the minimum charge estimation according to an embodiment of the present invention
• Fig. 2 is a schematic block diagram showing the sensor components of a power estimating embodiment of the present invention
• Fig. 3 is an example plot of open-circuit-voltage (OCV) as a function of state-of-charge for a particular cell electrochemistry
• Fig. 1A is a flow chart that outlines the
• Fig. 4 is an example plot showing the derivative of OCV as a function of state-of-charge for a particular cell electrochemistry
• Fig. 5 is a plot showing the voltage prediction using the cell model of the present invention
• Fig 6 is a zoom-in of the plot of voltage prediction for one UDDS cycle at around 50% state-of-charge
• Fig. 7 is a state-of-charge trace for cell test
• Fig. 8 is a plot comparing static maximum power calculations as functions of SOC for the PNGV HPPC method and Method I of the present invention
• Fig. 9 is a plot showing that discharge power capability estimates for cell cycle test comprising sixteen UDDS cycles over an SOC range of 90% down to 10%
• Fig. 10 is zoomed-in plot of Fig.
• the present invention relates to a method and an apparatus for estimating discharge and charge power of battery applications, including battery packs used in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) .
• One embodiment is a charge prediction method that incorporates voltage, state-of-charge, power, and current design constraints, works for a user-specified prediction horizon ⁇ t, and is more robust and precise than the state of the art.
• the embodiment has the option of allowing different modeling parameters during battery operation to accommodate highly dynamic batteries used in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) where such previous implementations were difficult.
• An embodiment of the present invention calculates maximum charge/discharge power by calculating the maximum charge/discharge current using any combination of four primary limits: 1. state-of-charge (SOC) limits 2. voltage limits 3. current limits 4. power limits In one embodiment, the minimum absolute charge/discharge current value from the calculations using state-of-charge (SOC) , voltage, and current limits is then chosen to obtain the maximum absolute charge/discharge power. In one embodiment, the maximum absolute charge/discharge power is checked to ensure it is within the power limits.
• the maximum absolute charge/discharge power is calculated in a way as to not violate any combination of the limits that may be used.
• Prior methods do not use SOC limits in their estimation of maximum charge/discharge power.
• the present invention incorporates the SOC of the battery cell or battery pack to estimate the maximum charge/discharge current.
• the estimation explicitly includes a user-defined time horizon ⁇ t.
• the SOC is obtained by using a Kalman filter.
• the SOC that is produced by Kalman filtering also yields an estimate of the uncertainty value, which can be used in the maximum charge/discharge calculation to yield a confidence level of the maximum charge/discharge current estimate.
• voltage limits are used to calculate the maximum charge/discharge current in a way that includes a user- defined time horizon ⁇ t.
• Two primary cell model embodiments are in the present invention for the calculation of maximum charge/discharge power based on voltage limits.
• the first is a simple cell model that uses a Taylor-series expansion to linearize the equation involved.
• the second is a more complex and accurate cell model that models cell dynamics in discrete-time state-space form.
• the cell model can incorporate a variety of inputs such as temperature, resistance, capacity, etc.
• One advantage of using model- based approach is that the same cell model may be used in both Kalman filtering to produce the SOC and the estimation of maximum charge/discharge current based of voltage limits.
• Embodiments of the present invention also include methods of charge estimation based on any combination of the voltage, current, power, or SOC limits described above. For example, charge estimation can be based on voltage limits only, or combined with current limits, SOC limits and/or power limits.
• Embodiments of the present invention are directed to a power apparatus that takes in data measurements from the battery such as current, voltage, temperature, and feeding such measurements to an arithmetic circuit, which includes calculation means that performs the calculation methods disclosed in the present invention to estimate the absolute maximum charge or discharge power.
• Embodiments of the present invention relates to battery charge estimation for any battery-powered application.
• the estimator method and apparatus find the maximum absolute battery charge and/or discharge power (based on present battery pack conditions) that may be maintained for ⁇ t seconds without violating pre-set limits on cell voltage, state-of-charge, power, or current.
• Figs. 1A and IB illustrates an overview of the embodiments of the present invention.
• Fig. 1A shows a method for finding the maximum discharge power for a user- defined time horizon ⁇ t, i.e. how much power may be drawn from the battery continuously for use for the next ⁇ t time period.
• accurate estimation of maximum discharge power can help prevent the hazardous occurrence of over-drawing the battery.
• the maximum discharge current is calculated based on pre-set limits on state-of-charge.
• the estimation explicitly includes a user-defined time horizon ⁇ t.
• the SOC is obtained by using a kalman filtering method.
• the SOC that is produced by Kalman filtering also yields an estimate of the uncertainty value, which can be used in the maximum charge/discharge calculation to yield a confidence level of the maximum charge/discharge current estimation.
• a simple state-of-charge is used. Step 10 is further described in the section titled "Calculation Based on State-of-Charge (SOC) Limits.”
• SOC State-of-Charge
• the present invention has two main model embodiments for calculation of maximum charge/discharge power based on voltage limits, although it is understood that other cell models could be used. Both overcome the limitation of prior art discharge estimation methods of giving a crude prediction of time horizon ⁇ t.
• the first is a simple cell model that uses a Taylor-series expansion to linearize the equation involved.
• the second is a more complex and accurate cell model that models cell dynamics in discrete-time state-space form.
• the cell model can incorporate a variety of inputs such as temperature, resistance, capacity, etc.
• the two cell models are further described in the section titled "Calculation Based on Voltage Limits.” Then In step 14, the maximum discharge current is calculated based on pre-set limits on current.
• step 16 the minimum of the three calculated current values from steps 10, 12, and 14 is chosen. It is understood that the execution order of steps 10, 12, 14 is interchangeable. It is further understood that any combination of steps 10, 12, and 14 may be omitted, if desired, in an implementation.
• step 18 calculates the maximum discharge power. The calculated pack power may be further refined in order to not violate individual cell or battery pack power design limits.
• Fig. IB shows a method for finding the maximum absolute charge power for a user-defined time horizon ⁇ t, i.e. how much power can be put back into the battery continuously for the next ⁇ t time period. The details and progression of the method mirror that of Fig. 1A.
• the maximum absolute current is the minimum current in a signed sense.
• the minimum charge current is calculated based on preset limits on state-of-charge. Again the SOC can be a simple one or one obtained using the Kalman filtering method. Then the minimum charge current is calculated based on pre-set limits on voltage in step 22 in accordance with a cell model, such as one of the two cell models described in the present disclosure. Then in step 24, the minimum charge current is calculated based on pre-set limits on current. Then, in step 26, the maximum of the three calculated current values from steps 20, 22, 24 is chosen. Note again that the execution order of steps 20, 22, 24 is interchangeable.
• step 28 calculates the minimum charge power.
• the calculated pack power may be further refined in order to not violate individual cell or battery pack power design limits. It is noted that modifications may be made to the method embodiments as shown in Figs. 1A and IB. For example, any or all of the current calculation steps based on state-of- charge and voltage limits may be removed. Also, the present invention discloses several methods of calculating maximum absolute charge and discharge current based on state-of- charge, voltage limits, and current limits. One embodiment of the present invention estimates the maximum absolute charge and/or discharge power of a battery pack.
• the battery pack may be, for example, a battery pack used in a hybrid electric vehicle or an electric vehicle.
• the embodiment makes a number of denotations and limits, including: - using n to denote the number of cells in the target battery pack where an estimation of charge and/or discharge power is desired; - using j t (t) to denote the cell voltage for cell number k in the pack, which has operational design limits so that Vmin ⁇ Vk(t) ⁇ «m « must be enforced for all k : I ⁇ k ⁇ n; - using z k ( t) to denote the state-of-charge for cell number k in the pack, which has operational design limits 2min ⁇ z k (t) ⁇ z max that must be enforced for all : 1 ⁇ k ⁇ n; - using p k ( t) to denote the cell power, which has a operational design limits so that P m ⁇ Pfc(*) ⁇ P ma must be enforced for
• any particular limit may be removed if desired by replacing its value by ⁇ , as appropriate.
• limits such as v max t v m ⁇ n , z max ⁇ z m ⁇ n r i max r i mxn, ⁇ max r p m ⁇ n may furthermore be functions of temperature and other factors pertaining to the present battery pack operating condition. In one embodiment, it is assumed that the discharge current and power have positive sign and the charge current and power have negative sign. Those skilled in the art will recognize that other sign conventions may be used, and that the description of the present invention can be adapted to these conventions in a forthright manner.
• the model used for predicting charge assumes that the battery pack comprises n s cell modules connected in series, where each cell module comprises n p individual cells connected in parallel and n s _ l, n v _ l.
• Fig. 2 is a schematic block diagram showing the sensor components of an embodiment of the present invention.
• Battery 40 is connected to load circuit 48.
• load circuit 48 could be a motor in an Electric Vehicle (EV) or Hybrid Electric Vehicle (HEV) .
• circuit 48 is a circuit that provides power and/or draws power. Measurements of battery and individual cell voltage are made with voltmeter (s) 44. Measurements of battery current are made with ammeter 42.
• Temperatur sensor (s) 46 Voltage, current and temperature measurements are processed with arithmetic circuit 50.
• Arithmetic circuit (estimator means) 50 takes in the measurements from the sensor components and perform the calculation methods of the present invention for power estimation. In some embodiment, temperature is not needed in the calculation methods.
• the power predictive method can take into account more information than simply the cell SOC.
• a Kalman filter can be used as a method to estimate all the cell SOCs in a pack. Besides giving the SOC, Kalman filtering yields estimates of the uncertainty of the SOC estimate itself.
• a method of using Kalman filter to estimate SOC is described in commonly assigned U.S. Patent No. 6,534,954, hereby incorporated by reference. Let the uncertainty have Gaussian distribution with standard deviation, as estimated by the Kalman filter, be denoted as ⁇ z .
• the method yields a95.5% confidence that the true SOC is within the estimate ⁇ 2 ⁇ z and a 99.7% confidence that the true SOC is within the estimate ⁇ 3 ⁇ z .
• This information can be incorporated into the estimate of maximum current based on SOC to have very high confidence that SOC design limits will not be violated. This is done as (assuming a 3 ⁇ z confidence interval) :
• embodiments of the present invention correct a limitation in the prior art HPPC method for applying voltage limits (steps 12 and 22 of Figs. 1A and IB) .
• HPPC method if the cell model of equation (1) is assumed, and that R chg and R dls are the cell's Ohmic resistances, then equation (2) and equation (3) predict the instantaneously available current, not the constant value of current that is available for the next ⁇ t seconds. If cases where ⁇ t is large, the result of the calculation poses a safety or battery-health issue, as the cells may become over/under charged.
• both the function OCV(z) and its derivative dOCV(z)/ dz might be computed from some known mathematical relationship for OCV(z), (e.g., Nernst's equation) using either analytic or numeric methods, or by a table lookup of empirical data.
• This quantity is positive for most battery electrochemistries over the entire SOC range, so the values computed by (8) and (9) are smaller in magnitude than those from (2) and (3) for the same values of R ⁇ is and R chq .
• the HPPC procedure compensates for its inaccuracy by using modified values of R dls and J? chg , determined experimentally, that approximate the denominator terms in (8) and (9) .
• n. PH n p ⁇ i v k (t + t) ⁇ iS flare ( OCV (z k (t) - d ⁇ iAt/C
• OCV ( z) , C, v maxr v m ⁇ n r z maxr z m ⁇ n r i max , i mx alloy, R chg , and R dls may be functions of temperature and other factors pertaining to the present battery pack operating conditions.
• a second method embodiment of the present invention may be used when more computational power is available.
• An example Cell Model An example cell model for the present invention power estimation methods is presented herein, with illustrations given to show the performance of the two methods compared to the prior art PNGV HPPC method.
• the cell model is a discrete-time state-space model of the form of (14) and (15) that applies to battery cells.
• the model named "Enhanced Self-Correcting Cell Model,” is further described in the article “Advances in EKFLiPB SOC Estimation,” by the inventor, published in CD-ROM and presented in Proc. 20th Electric Vehicle Symposium (EVS20) in Long Beach CA, (November 2003) and is hereby fully incorporated by reference. It is understood this model is an example model only and that a variety of suitable alternate models can be used.
• the "Enhanced Self-Correcting Cell Model” includes effects due to open-circuit-voltage, internal resistance, voltage time constants, and hysteresis.
• the parameter values are fitted to this model structure to model the dynamics of high-power Lithium-Ion Polymer Battery (LiPB) cells, although the structure and methods presented here are general.
• State-of-charge is captured by one state of the model.
• the matrix * c may be a diagonal matrix with real-valued entries. If so, the system is stable if all entries have magnitude less than one.
• the vector Bf E K ⁇ may simply be set to n f "l"s. The value of n f and the entries in the A f matrix are chosen as part of the system identification procedure to best fit the model parameters to measured cell data. The hysteresis level is captured by a single state
• the open-circuit-voltage as a function of state-of- charge for example Lithium Ion Polymer Battery (LiPB) cells is plotted in Fig.3. This is an empirical relationship found by cell testing. First, the cell was fully charged (constant current to 4.2V, constant voltage to 200mA).
• the cell was discharged at the C/25 rate until fully discharged (3.0V).
• the cell was then charged at the C/25 rate until the voltage was 4.2V.
• the low rates were used to minimize the dynamics excited in the cells.
• the cell voltage as a function of state of charge under discharge and under charge were averaged to compute the OCV. This has the effect of eliminating to the greatest extent possible the presence of hysteresis and ohmic resistance in the final function.
• the final curve was digitized at 200 points and stored in a table. Linear interpolation is used to look up values in the table.
• the partial derivative of OCV with respect to SOC for these example cells is plotted in Fig. 4.
• Fig. 5 is a plot showing the voltage prediction using the cell model of the present invention.
• the cell test was a sequence of sixteen UDDS cycles, performed at room temperature, separated by discharge pulses and five-minute rests, and spread over the 90% to 10% SOC range.
• the difference between true cell terminal voltage and estimated cell terminal voltage is very small (a root-mean-squared (RMS) voltage estimation error of less than 5mV) .
• RMS root-mean-squared
• Fig. 7 is a SOC trace for cell test.
• the graph shows that SOC increases by about 5% during each UDDS cycle, but is brought down about 10% during each discharge between cycles.
• the entire operating range for these cells (10% SOC to 90% SOC, delineated on the figure as the region between the thin dashed lines) is excited during the cell test.
• the PNGV HPPC power estimation method gives a result that is a function of only SOC. Therefore, it is possible to graph available power versus SOC to summarize the algorithm calculations.
• the first method proposed (Method I: Taylor Series Expansion Methods) in this patent disclosure is also possible to display in this way. Estimated power is only a function of SOC, 30CV/dz (also a function of SOC) , and static limits on maximum current and power.
• the second method (Method II: the Comprehensive Cell Model Method) , however, dynamically depends on all states of the system. Two systems at the same state of charge, but with different voltage time-constant state values or hysteresis state levels will have different amounts of power available.
• Fig. 8 is a plot comparing static maximum power calculations as functions of SOC for the PNGV HPPC method and Method I of the present invention.
• the black curves correspond to charge power
• the gray curves correspond to discharge power. Note that the absolute value of power is plotted to avoid confusion due to sign conventions.
• the PNGV HPPC method produces similar values to Method I in the mid-SOC range. The slight differences are due to the fact that the 10- second R chg value used for the PNGV method and the derivative-modified R chg for Method I are not identical.
• the graph shows that Method I ramps power down in the neighborhood of z max to avoid over- charging the cell, whereas the PNGV method has no such limits.
• the PNGV method over-predicts how much power is available since there are no current limits applied to the calculation.
• the Method I estimate is automatically lower due to the large derivative in the denominator of the calculation. This causes an anomaly near zero SOC where the method under-predicts the available charge power.
• the discharge power curves the comparison shows that Method I imposes limits on discharge power to ensure that the cell is not under-charged, whereas the PNGV method does not.
• Figs. 9 through 13 show how the two main voltage-limit based methods of power estimation of the present invention (Method I and Method II) compare to the prior art PNGV method in the dynamic cell tests shown in Fig. 5. Fig.
• FIG. 9 is a plot showing that discharge power capability estimates for cell cycle test comprising sixteen UDDS cycles over an SOC range of 90% down to 10%.
• Fig. 10 is zoomed-in plot of Fig. 9, showing about one UDDS cycle.
• Fig. 11 is a plot showing charging power capability estimates for cell cycle test comprising sixteen UDDS cycles over an SOC range of 90% down to 10%.
• Fig. 12 is zoomed-in plot of Fig. 11, showing about one UDDS cycle. Again, the absolute value of power is plotted.
• the results of Method II are considered to be the "true" capability of the cell. This assumption is justified by the fidelity of the cell model's voltage estimates, as supported by the data in Fig. 6.
• Fig. 9 shows that the three methods produce similar estimates.
• Method I and Method II appear to be nearly identical when viewed at this scale.
• the PNGV HPPC method predicts higher power than is actually available (by as much as 9.8%), and at low SOCs, the PNGV HPPC method under-predicts the available power. Only the methods of the present invention include SOC bounds, which explain why their predictions are so different from the
• PNGV HPPC estimates at low SOC. If the vehicle controller were to discharge at the rates predicted by the PNGV HPPC method, the cell would be over-discharged in some cases (lowering its lifetime), and under-utilized in other cases.
• Fig. 10 zooms in on Fig. 9 (same region shown as in Fig. 6) to show greater detail. In this region, the three methods produce nearly identical predictions.
• a notable feature of Method II, however, is that it takes into account the entire dynamics of the cell when making a prediction. Therefore, the strong discharges at around time 237 and 267 minutes draw the cell voltage down, and allows less discharge power than the other two methods which only consider SOC when making their estimate. The three methods are also compared with respect to charge power, shown in Fig. 11.

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